






"""simple docstring"""


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE   =   logging.get_logger('transformers.models.speecht5')


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						hf_model.apply_weight_norm()

						lowerCAmelCase   :int						=			checkpoint['input_conv.weight_g']
						lowerCAmelCase   :Tuple						=			checkpoint['input_conv.weight_v']
						lowerCAmelCase   :Dict						=			checkpoint['input_conv.bias']

						for i in range(len(config.upsample_rates ) ):
												lowerCAmelCase   :Union[str, Any]						=			checkpoint[F"""upsamples.{i}.1.weight_g"""]
												lowerCAmelCase   :List[Any]						=			checkpoint[F"""upsamples.{i}.1.weight_v"""]
												lowerCAmelCase   :int						=			checkpoint[F"""upsamples.{i}.1.bias"""]

						for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
												for j in range(len(config.resblock_dilation_sizes ) ):
																		lowerCAmelCase   :int						=			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
																		lowerCAmelCase   :Optional[Any]						=			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
																		lowerCAmelCase   :str						=			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

																		lowerCAmelCase   :str						=			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																		lowerCAmelCase   :List[str]						=			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																		lowerCAmelCase   :Any						=			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

						lowerCAmelCase   :Tuple						=			checkpoint['output_conv.1.weight_g']
						lowerCAmelCase   :Tuple						=			checkpoint['output_conv.1.weight_v']
						lowerCAmelCase   :str						=			checkpoint['output_conv.1.bias']

						hf_model.remove_weight_norm()


@torch.no_grad()
def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__=None     ,      a__=None     ,      ):





						'''simple docstring'''






						if config_path is not None:
												lowerCAmelCase   :Union[str, Any]						=			SpeechTaHifiGanConfig.from_pretrained(a__ )
						else:
												lowerCAmelCase   :List[str]						=			SpeechTaHifiGanConfig()

						lowerCAmelCase   :str						=			SpeechTaHifiGan(a__ )

						lowerCAmelCase   :List[Any]						=			torch.load(a__ )
						load_weights(orig_checkpoint['model']['generator']     ,      a__     ,      a__ )

						lowerCAmelCase   :List[str]						=			np.load(a__ )
						lowerCAmelCase   :List[Any]						=			stats[0].reshape(-1 )
						lowerCAmelCase   :Optional[int]						=			stats[1].reshape(-1 )
						lowerCAmelCase   :Union[str, Any]						=			torch.from_numpy(a__ ).float()
						lowerCAmelCase   :Tuple						=			torch.from_numpy(a__ ).float()

						model.save_pretrained(a__ )

						if repo_id:
												print('Pushing to the hub...' )
												model.push_to_hub(a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
		parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
		)

		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_hifigan_checkpoint(
		    args.checkpoint_path,
		    args.stats_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")